PurposeTo differentiate the plasma metabolomic profile of patients with age related macular degeneration (AMD) from that of controls, by Nuclear Magnetic Resonance (NMR) spectroscopy.MethodsTwo cohorts (total of 396 subjects) representative of central Portugal and Boston, USA phenotypes were studied. For each cohort, subjects were grouped according to AMD stage (early, intermediate and late). Multivariate analysis of plasma NMR spectra was performed, followed by signal integration and univariate analysis.ResultsSmall changes were detected in the levels of some amino acids, organic acids, dimethyl sulfone and specific lipid moieties, thus providing some biochemical information on the disease. The possible confounding effects of gender, smoking history and age were assessed in each cohort and found to be minimal when compared to that of the disease. A similar observation was noted in relation to age-related comorbidities. Furthermore, partially distinct putative AMD metabolite fingerprints were noted for the two cohorts studied, reflecting the importance of nutritional and other lifestyle habits in determining AMD metabolic response and potential biomarker fingerprints. Notably, some of the metabolite changes detected were noted as potentially differentiating controls from patients diagnosed with early AMD.ConclusionFor the first time, this study showed metabolite changes in the plasma of patients with AMD as compared to controls, using NMR. Geographical origins were seen to affect AMD patients´ metabolic profile and some metabolites were found to be valuable in potentially differentiating controls from early stage AMD patients. Metabolomics has the potential of identifying biomarkers for AMD, and further work in this area is warranted.

This work assesses the urinary metabolite signature of prematurity in newborns by nuclear magnetic resonance (NMR) spectroscopy, while establishing the role of possible confounders and signature specificity, through comparison to other disorders. Gender and delivery mode are shown to impact importantly on newborn urine composition, their analysis pointing out at specific metabolite variations requiring consideration in unmatched subject groups. Premature newborns are, however, characterized by a stronger signature of varying metabolites, suggestive of disturbances in nucleotide metabolism, lung surfactants biosynthesis and renal function, along with enhancement of tricarboxylic acid (TCA) cycle activity, fatty acids oxidation, and oxidative stress. Comparison with other abnormal conditions (respiratory depression episode, large for gestational age, malformations, jaundice and premature rupture of membranes) reveals that such signature seems to be largely specific of preterm newborns, showing that NMR metabolomics can retrieve particular disorder effects, as well as general stress effects. These results provide valuable novel information on the metabolic impact of prematurity, contributing to the better understanding of its effects on the newborn's state of health.

Metabolic biomarkers of pre- and postdiagnosis gestational diabetes mellitus (GDM) were sought, using nuclear magnetic resonance (NMR) metabolomics of maternal plasma and corresponding lipid extracts. Metabolite differences between controls and disease were identified through multivariate analysis of variable selected (1)H NMR spectra. For postdiagnosis GDM, partial least squares regression identified metabolites with higher dependence on normal gestational age evolution. Variable selection of NMR spectra produced good classification models for both pre- and postdiagnostic GDM. Prediagnosis GDM was accompanied by cholesterol increase and minor increases in lipoproteins (plasma), fatty acids, and triglycerides (extracts). Small metabolite changes comprised variations in glucose (up regulated), amino acids, betaine, urea, creatine, and metabolites related to gut microflora. Most changes were enhanced upon GDM diagnosis, in addition to newly observed changes in low-Mw compounds. GDM prediction seems possible exploiting multivariate profile changes rather than a set of univariate changes. Postdiagnosis GDM is successfully classified using a 26-resonance plasma biomarker. Plasma and extracts display comparable classification performance, the former enabling direct and more rapid analysis. Results and putative biochemical hypotheses require further confirmation in larger cohorts of distinct ethnicities.

Objectives Saliva metabolome is a promising diagnostic tool concerning oral and systemic diseases. We aimed at establishing a suitable protocol for saliva collection and gauging the relative impacts of gender, dentition stage, and caries on the saliva metabolome of a small children cohort. Subjects and methods A nuclear magnetic resonance‐based metabolomics cross‐sectional study of children saliva (n = 38) compared the effects of: (a) stimulation and unstimulation conditions, and (b) collection through passive drool and using an absorbing device. Multivariate and univariate statistical analyses were applied to evaluate such effects and those related to gender, dentition stage and caries. Results No significant differences were found between unstimulated and stimulated saliva, and the former was used for subsequent studies. Swab collection induced significant changes in sample composition, indicating passive drool as preferential. The impacts of gender and dentition stage were not significant compared to that of caries, which induced variations in the levels of 21 metabolites. These comprised amino acids and monosaccharides observed for the first time to our knowledge regarding children caries, suggesting protein hydrolysis and deglycosylation. Conclusions Unstimulated passive drool saliva metabolome may carry a caries signature.

Biofluid biomarkers of age-related macular degeneration (AMD) are still lacking, and their identification is challenging. Metabolomics is well-suited to address this need, and urine is a valuable accessible biofluid. This study aimed to characterize the urinary metabolomic signatures of patients with different stages of AMD and a control group (>50 years). It was a prospective, cross-sectional study, where subjects from two cohorts were included: 305 from Coimbra, Portugal (AMD patients n = 252; controls n = 53) and 194 from Boston, United States (AMD patients n = 147; controls n = 47). For all participants, we obtained color fundus photographs (for AMD staging) and fasting urine samples, which were analyzed using 1H nuclear magnetic resonance (NMR) spectroscopy. Our results revealed that in both cohorts, urinary metabolomic profiles differed mostly between controls and late AMD patients, but important differences were also found between controls and subjects with early AMD. Analysis of the metabolites responsible for these separations revealed that, even though distinct features were observed for each cohort, AMD was in general associated with depletion of excreted citrate and selected amino acids at some stage of the disease, suggesting enhanced energy requirements. In conclusion, NMR metabolomics enabled the identification of urinary signals of AMD and its severity stages, which might represent potential metabolomic biomarkers of the disease.